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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Finding disease similarity based on implicit semantic similarity.

Sachin Mathur1, Deendayal Dinakarpandian

  • 1School of Computing & Engineering, University of Missouri-Kansas City, 5100 Rockhill Rd., Kansas City, MO 64110, USA.

Journal of Biomedical Informatics
|December 15, 2011
PubMed
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Analyzing shared biological processes, not just genes, improves disease similarity detection. This approach enhances understanding of disease origins, shared mechanisms, and potential cross-treatments for complex conditions.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Genomic data enables gene-disease and gene-function annotations for studying disease similarity.
  • Relying solely on shared genes for disease similarity can be misleading, especially for complex diseases.
  • Identifying common biological processes offers a more robust approach to understanding disease relationships.

Purpose of the Study:

  • To develop and evaluate a novel method for estimating disease similarity using semantic similarity of biological processes.
  • To compare the performance of process-based similarity with gene-overlap methods.

Main Methods:

  • Utilized Gene Ontology (GO) to represent biological processes.
  • Developed functions to measure semantic similarity between GO terms based on co-occurrence and information content.
  • Benchmarked against gene-based comparisons using a curated dataset of known disease similarities.

Main Results:

  • Semantic similarity of GO Processes outperformed exact GO Process matching and gene overlap in detecting known disease similarities (Recall=55%, Precision=60%).
  • GO-Process based similarity scores correlated significantly (Pearson r=0.73) with expert assessments.
  • Identified significantly regulated GO Processes in microarray datasets of related diseases.

Conclusions:

  • Semantic similarity of biological processes is a superior metric for estimating disease similarity compared to gene overlap.
  • This approach enhances disease characterization, aids in understanding etiology and pathophysiology, and suggests potential therapeutic strategies.
  • Findings support the utility of GO-based semantic similarity in biomedical research and clinical applications.